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Tongji University Team for the VoxCeleb Speaker Recognition Challenge 2020
In this report, we describe the submission of Tongji University team to ...
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Tongji University Undergraduate Team for the VoxCeleb Speaker Recognition Challenge2020
In this report, we discribe the submission of Tongji University undergra...
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The IDLAB VoxCeleb Speaker Recognition Challenge 2020 System Description
In this technical report we describe the IDLAB top-scoring submissions f...
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BUT System Description to VoxCeleb Speaker Recognition Challenge 2019
In this report, we describe the submission of Brno University of Technol...
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Microsoft Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2020
This paper describes the Microsoft speaker diarization system for monaur...
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Third DIHARD Challenge Evaluation Plan
This paper introduces the third DIHARD challenge, the third in a series ...
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BUT VOiCES 2019 System Description
This is a description of our effort in VOiCES 2019 Speaker Recognition c...
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The xx205 System for the VoxCeleb Speaker Recognition Challenge 2020
This report describes the systems submitted to the first and second tracks of the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2020, which ranked second in both tracks. Three key points of the system pipeline are explored: (1) investigating multiple CNN architectures including ResNet, Res2Net and dual path network (DPN) to extract the x-vectors, (2) using a composite angular margin softmax loss to train the speaker models, and (3) applying score normalization and system fusion to boost the performance. Measured on the VoxSRC-20 Eval set, the best submitted systems achieve an EER of 3.808% and a MinDCF of 0.1958 in the close-condition track 1, and an EER of 3.798% and a MinDCF of 0.1942 in the open-condition track 2, respectively.
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